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Oct 12 · Monday
Another 254 papers do not have category tags yet and are not included in filtered results.
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About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
提出轨迹级可迁移对抗攻击,降低深度强化学习策略的期望回报
Published Oct 5
Evaluated models DQN , Double DQN (DDQN) Abstract 轨迹级后退视界攻击在 CartPole 上比逐步 FGSM 类和 Sign 噪声更压低回报。这项工作研究 DRL 上的迁移式黑盒观测攻击 :受害参数不可访问时,在白盒代理上造扰动再迁移。
提出利用共享冷却的跨租户干扰攻击,并设计租户隔离机制
Published Oct 4 Abstract 揭示网关共享冷却漏洞,提出来源检查与租户隔离防御。
构造可通过计费审计的 token 虚报,使服务商系统性超收
Published May 28
Evaluated models PALACE domain-adapted auditor Abstract 不诚实提供方可在三种 token 审计上虚报并通过检查,作者认为需改用提供方不控制的证据。不透明 LLM 的按 token 计费里,现有审计核验的是提供方可控的证据,不诚实提供方可以虚报并通过检查。 。
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